Bibliographic record
Abstract
This paper describes how the practice of operating buses on highway shoulders to bypass congestion is a potentially effective use of limited infrastructure. Learning from a few tentative initiatives in the 1990s, transportation planners, highway engineers, and transit operators are beginning to take the Bus-on-Shoulder option seriously. However, many areas are thinking cautiously in terms of a restricted-use type of Bus-on-Shoulder operation, with severe limitations on operating speed, the time of use, and eligible users and this limits the potential advantage for bus operators. Furthermore, moving shoulder buses safely and efficiently past interchange ramps is always a challenge. For a leading example of how the Bus-on-Shoulder strategy can reach its fullest potential, this paper looks to Ottawa, Canada where, in 1992, up to 100 buses per hour have been using shoulders in an unrestricted high-speed environment on a four-lane freeway. Innovative treatments at interchanges have played a significant role in the facility’s success. This operation has saved thousands of hours of travel time, made efficient use of both the bus fleet and the highway space, and deferred tens of millions of dollars of investment that would otherwise be required in dedicated transit infrastructure. This paper outlines the design and operational features of Ottawa’s Bus-on-Shoulder facility, and places it in the context of other Bus-on-Shoulder projects that are currently in operation worldwide. A comparative analysis is made between the successful operational strategy used in Ottawa and the more restricted practices elsewhere.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".